Abstract
We introduce the task of Multi-Modal Machine Comprehension (M3C), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question Answering (TQA) dataset that includes 1,076 lessons and 26,260 multi-modal questions, taken from middle school science curricula. Our analysis shows that a significant portion of questions require complex parsing of the text and the diagrams and reasoning, indicating that our dataset is more complex compared to previous machine comprehension and visual question answering datasets. We extend state-of-the-art methods for textual machine comprehension and visual question answering to the TQA dataset. Our experiments show that these models do not perform well on TQA. The presented dataset opens new challenges for research in question answering and reasoning across multiple modalities.
Original language | English |
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Title of host publication | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 5376-5384 |
Number of pages | 9 |
ISBN (Electronic) | 9781538604571 |
DOIs | |
Publication status | Published - 2017 Nov 6 |
Event | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: 2017 Jul 21 → 2017 Jul 26 |
Publication series
Name | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
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Volume | 2017-January |
Other
Other | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
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Country/Territory | United States |
City | Honolulu |
Period | 17/7/21 → 17/7/26 |
Bibliographical note
Funding Information:This work is in part supported by ONR N00014-13-1-0720, NSF IIS-1338054, NSF-1652052, NRI-1637479, NSF IIS-1616112, Allen Distinguished Investigator Award, Google Research Faculty Award, Samsung GRO Award and the Allen Institute for Artificial Intelligence.
Publisher Copyright:
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition